Recensement

Dataviz sur les données du recensement 2019 avec recoupement sur les logements

!pip install pandas seaborn dataprep
Requirement already satisfied: pandas in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (1.4.2)
Requirement already satisfied: seaborn in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (0.11.2)
Requirement already satisfied: dataprep in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (0.4.3)
Requirement already satisfied: numpy>=1.18.5 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from pandas) (1.22.4)
Requirement already satisfied: pytz>=2020.1 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from pandas) (2022.1)
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Requirement already satisfied: matplotlib>=2.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from seaborn) (3.5.2)
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Requirement already satisfied: python-Levenshtein<0.13.0,>=0.12.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from dataprep) (0.12.2)
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Requirement already satisfied: nltk<4.0.0,>=3.6.7 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from dataprep) (3.7)
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Requirement already satisfied: soupsieve>1.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.3.2.post1)
Requirement already satisfied: webencodings in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.5.1)
Requirement already satisfied: pycparser in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.21)
WARNING: There was an error checking the latest version of pip.

!wget "https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_individus.csv
--2022-05-24 05:23:43--  https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 
13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_individus.csv’


          data/rece     [<=>                 ]       0  --.-KB/s               
         data/recen     [ <=>                ]  36.68K   182KB/s               
        data/recens     [  <=>               ] 100.67K   250KB/s               
       data/recense     [   <=>              ] 212.64K   352KB/s               
      data/recensem     [    <=>             ] 308.63K   311KB/s               
     data/recenseme     [     <=>            ] 388.61K   295KB/s               
    data/recensemen     [      <=>           ] 500.59K   297KB/s               
   data/recensement     [       <=>          ] 596.57K   296KB/s               
  data/recensement_     [        <=>         ] 676.55K   287KB/s               
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data/recensement_in     [          <=>       ] 860.55K   286KB/s               
ata/recensement_ind     [           <=>      ] 924.55K   278KB/s               
ta/recensement_indi     [            <=>     ]   1005K   274KB/s               
a/recensement_indiv     [             <=>    ]   1.09M   280KB/s               
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recensement_individ     [               <=>  ]   1.25M   275KB/s               
ecensement_individu     [                <=> ]   1.32M   265KB/s               
censement_individus     [                 <=>]   1.43M   263KB/s               
ensement_individus.     [                <=> ]   1.45M   254KB/s               
nsement_individus.c     [               <=>  ]   1.50M   246KB/s               
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        data/recens     [                 <=>]   3.00M   134KB/s               
       data/recense     [                <=> ]   3.03M   130KB/s               
      data/recensem     [               <=>  ]   3.08M   135KB/s               
     data/recenseme     [              <=>   ]   3.12M   132KB/s               
    data/recensemen     [             <=>    ]   3.15M   130KB/s               
   data/recensement     [            <=>     ]   3.18M   132KB/s               
  data/recensement_     [           <=>      ]   3.25M   132KB/s               
 data/recensement_i     [          <=>       ]   3.28M   130KB/s               
data/recensement_in     [         <=>        ]   3.31M   124KB/s               
ata/recensement_ind     [        <=>         ]   3.37M   129KB/s               
ta/recensement_indi     [       <=>          ]   3.40M   128KB/s               
a/recensement_indiv     [      <=>           ]   3.43M   127KB/s               
/recensement_indivi     [     <=>            ]   3.50M   133KB/s               
recensement_individ     [    <=>             ]   3.53M   135KB/s               
ecensement_individu     [   <=>              ]   3.56M   132KB/s               
censement_individus     [  <=>               ]   3.62M   136KB/s               
ensement_individus.     [ <=>                ]   3.65M   135KB/s               
nsement_individus.c     [<=>                 ]   3.68M   130KB/s               
sement_individus.cs     [ <=>                ]   3.78M   134KB/s               
ement_individus.csv     [  <=>               ]   3.81M   135KB/s               
ment_individus.csv      [   <=>              ]   3.87M   137KB/s               
ent_individus.csv       [    <=>             ]   3.90M   142KB/s               
nt_individus.csv        [     <=>            ]   3.93M   136KB/s               
t_individus.csv         [      <=>           ]   4.00M   139KB/s               
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individus.csv           [        <=>         ]   4.06M   134KB/s               
ndividus.csv            [         <=>        ]   4.12M   132KB/s               
dividus.csv             [          <=>       ]   4.15M   135KB/s               
ividus.csv              [           <=>      ]   4.18M   134KB/s               
vidus.csv               [            <=>     ]   4.25M   136KB/s               
idus.csv                [             <=>    ]   4.28M   134KB/s               
dus.csv                 [              <=>   ]   4.31M   134KB/s               
us.csv                  [               <=>  ]   4.37M   131KB/s               
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.csv                    [                 <=>]   4.43M   133KB/s               
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                        [             <=>    ]   4.62M   136KB/s               
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             data/r     [       <=>          ]   4.87M   134KB/s               
            data/re     [      <=>           ]   4.90M   135KB/s               
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          data/rece     [    <=>             ]   5.00M   137KB/s               
         data/recen     [   <=>              ]   5.03M   136KB/s               
        data/recens     [  <=>               ]   5.09M   138KB/s               
       data/recense     [ <=>                ]   5.12M   138KB/s               
      data/recensem     [<=>                 ]   5.15M   138KB/s               
     data/recenseme     [ <=>                ]   5.21M   139KB/s               
    data/recensemen     [  <=>               ]   5.25M   138KB/s               
   data/recensement     [   <=>              ]   5.28M   137KB/s               
  data/recensement_     [    <=>             ]   5.34M   137KB/s               
 data/recensement_i     [     <=>            ]   5.37M   133KB/s               
data/recensement_in     [      <=>           ]   5.42M   133KB/s               
ata/recensement_ind     [       <=>          ]   5.46M   132KB/s               
ta/recensement_indi     [        <=>         ]   5.50M   133KB/s               
a/recensement_indiv     [         <=>        ]   5.53M   131KB/s               
/recensement_indivi     [          <=>       ]   5.59M   135KB/s               
recensement_individ     [           <=>      ]   5.62M   136KB/s               
ecensement_individu     [            <=>     ]   5.65M   132KB/s               
censement_individus     [             <=>    ]   5.71M   135KB/s               
ensement_individus.     [              <=>   ]   5.75M   135KB/s               
nsement_individus.c     [               <=>  ]   5.78M   126KB/s               
sement_individus.cs     [                <=> ]   5.82M   128KB/s               
ement_individus.csv     [                 <=>]   5.87M   128KB/s               
ment_individus.csv      [                <=> ]   5.93M   128KB/s               
ent_individus.csv       [               <=>  ]   5.96M   128KB/s               
nt_individus.csv        [              <=>   ]   6.00M   128KB/s               
t_individus.csv         [             <=>    ]   6.06M   124KB/s               
_individus.csv          [            <=>     ]   6.09M   125KB/s               
individus.csv           [           <=>      ]   6.12M   125KB/s               
ndividus.csv            [          <=>       ]   6.18M   124KB/s               
dividus.csv             [         <=>        ]   6.21M   126KB/s               
ividus.csv              [        <=>         ]   6.25M   124KB/s               
vidus.csv               [       <=>          ]   6.31M   126KB/s               
idus.csv                [      <=>           ]   6.34M   126KB/s               
dus.csv                 [     <=>            ]   6.37M   127KB/s               
us.csv                  [    <=>             ]   6.43M   127KB/s               
s.csv                   [   <=>              ]   6.46M   129KB/s               
.csv                    [  <=>               ]   6.50M   127KB/s               
csv                     [ <=>                ]   6.56M   130KB/s               
sv                      [<=>                 ]   6.59M   129KB/s               
v                       [ <=>                ]   6.65M   137KB/s               
                        [  <=>               ]   6.68M   134KB/s               
                  d     [   <=>              ]   6.71M   132KB/s               
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                dat     [     <=>            ]   6.81M   133KB/s               
               data     [      <=>           ]   6.84M   133KB/s               
              data/     [       <=>          ]   6.90M   140KB/s               
             data/r     [        <=>         ]   6.93M   138KB/s               
            data/re     [         <=>        ]   6.96M   139KB/s               
           data/rec     [          <=>       ]   7.03M   141KB/s               
          data/rece     [           <=>      ]   7.06M   142KB/s               
         data/recen     [            <=>     ]   7.09M   142KB/s               
        data/recens     [             <=>    ]   7.15M   142KB/s               
       data/recense     [              <=>   ]   7.21M   143KB/s               
      data/recensem     [               <=>  ]   7.28M   143KB/s               
     data/recenseme     [                <=> ]   7.31M   143KB/s               
    data/recensemen     [                 <=>]   7.37M   148KB/s               
   data/recensement     [                <=> ]   7.40M   143KB/s               
  data/recensement_     [               <=>  ]   7.43M   142KB/s               
 data/recensement_i     [              <=>   ]   7.50M   140KB/s               
data/recensement_in     [             <=>    ]   7.53M   140KB/s               
ata/recensement_ind     [            <=>     ]   7.56M   144KB/s               
ta/recensement_indi     [           <=>      ]   7.62M   144KB/s               
a/recensement_indiv     [          <=>       ]   7.65M   144KB/s               
/recensement_indivi     [         <=>        ]   7.68M   144KB/s               
recensement_individ     [        <=>         ]   7.75M   141KB/s               
ecensement_individu     [       <=>          ]   7.78M   142KB/s               
censement_individus     [      <=>           ]   7.81M   132KB/s               
ensement_individus.     [     <=>            ]   7.84M   129KB/s               
nsement_individus.c     [    <=>             ]   7.90M   125KB/s               
sement_individus.cs     [   <=>              ]   7.93M   124KB/s               
ement_individus.csv     [  <=>               ]   8.00M   121KB/s               
ment_individus.csv      [ <=>                ]   8.03M   116KB/s               
ent_individus.csv       [<=>                 ]   8.06M   113KB/s               
nt_individus.csv        [ <=>                ]   8.09M   109KB/s               
t_individus.csv         [  <=>               ]   8.15M   109KB/s               
_individus.csv          [   <=>              ]   8.18M   103KB/s               
individus.csv           [    <=>             ]   8.25M   106KB/s               
ndividus.csv            [     <=>            ]   8.28M   104KB/s               
dividus.csv             [      <=>           ]   8.34M   103KB/s               
ividus.csv              [       <=>          ]   8.37M   103KB/s               
vidus.csv               [        <=>         ]   8.40M   104KB/s               
idus.csv                [         <=>        ]   8.46M   103KB/s               
dus.csv                 [          <=>       ]   8.50M   102KB/s               
us.csv                  [           <=>      ]   8.53M   102KB/s               
s.csv                   [            <=>     ]   8.59M   103KB/s               
.csv                    [             <=>    ]   8.62M   103KB/s               
csv                     [              <=>   ]   8.65M   109KB/s               
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                dat     [              <=>   ]   8.93M   128KB/s               
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              data/     [            <=>     ]   9.00M   128KB/s               
             data/r     [           <=>      ]   9.06M   135KB/s               
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         data/recen     [       <=>          ]   9.21M   141KB/s               
        data/recens     [      <=>           ]   9.25M   140KB/s               
       data/recense     [     <=>            ]   9.31M   140KB/s               
      data/recensem     [    <=>             ]   9.34M   141KB/s               
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    data/recensemen     [  <=>               ]   9.43M   136KB/s               
   data/recensement     [ <=>                ]   9.46M   137KB/s               
  data/recensement_     [<=>                 ]   9.50M   135KB/s               
 data/recensement_i     [ <=>                ]   9.53M   135KB/s               
data/recensement_in     [  <=>               ]   9.59M   135KB/s               
ata/recensement_ind     [   <=>              ]   9.65M   140KB/s               
ta/recensement_indi     [    <=>             ]   9.68M   134KB/s               
a/recensement_indiv     [     <=>            ]   9.71M   135KB/s               
/recensement_indivi     [      <=>           ]   9.78M   135KB/s               
recensement_individ     [       <=>          ]   9.81M   133KB/s               
ecensement_individu     [        <=>         ]   9.84M   134KB/s               
censement_individus     [         <=>        ]   9.90M   134KB/s               
ensement_individus.     [          <=>       ]   9.93M   133KB/s               
nsement_individus.c     [           <=>      ]   9.96M   134KB/s               
sement_individus.cs     [            <=>     ]  10.03M   134KB/s               
ement_individus.csv     [             <=>    ]  10.06M   133KB/s               
ment_individus.csv      [              <=>   ]  10.09M   131KB/s               
ent_individus.csv       [               <=>  ]  10.15M   131KB/s               
nt_individus.csv        [                <=> ]  10.18M   129KB/s               
t_individus.csv         [                 <=>]  10.21M   130KB/s               
_individus.csv          [                <=> ]  10.28M   132KB/s               
individus.csv           [               <=>  ]  10.31M   130KB/s               
ndividus.csv            [              <=>   ]  10.34M   133KB/s               
dividus.csv             [             <=>    ]  10.40M   136KB/s               
ividus.csv              [            <=>     ]  10.43M   134KB/s               
vidus.csv               [           <=>      ]  10.46M   129KB/s               
idus.csv                [          <=>       ]  10.53M   133KB/s               
dus.csv                 [         <=>        ]  10.56M   133KB/s               
us.csv                  [        <=>         ]  10.59M   127KB/s               
s.csv                   [       <=>          ]  10.65M   133KB/s               
.csv                    [      <=>           ]  10.68M   133KB/s               
csv                     [     <=>            ]  10.71M   128KB/s               
sv                      [    <=>             ]  10.78M   131KB/s               
v                       [   <=>              ]  10.81M   130KB/s               
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                  d     [ <=>                ]  10.90M   130KB/s               
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                dat     [ <=>                ]  11.00M   132KB/s               
               data     [  <=>               ]  11.03M   133KB/s               
              data/     [   <=>              ]  11.06M   133KB/s               
             data/r     [    <=>             ]  11.12M   136KB/s               
            data/re     [     <=>            ]  11.15M   137KB/s               
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          data/rece     [       <=>          ]  11.25M   137KB/s               
         data/recen     [        <=>         ]  11.28M   136KB/s               
        data/recens     [         <=>        ]  11.31M   136KB/s               
       data/recense     [          <=>       ]  11.37M   137KB/s               
      data/recensem     [           <=>      ]  11.40M   137KB/s               
     data/recenseme     [            <=>     ]  11.43M   131KB/s               
    data/recensemen     [             <=>    ]  11.50M   131KB/s               
   data/recensement     [              <=>   ]  11.53M   129KB/s               
  data/recensement_     [               <=>  ]  11.56M   131KB/s               
 data/recensement_i     [                <=> ]  11.62M   130KB/s               
data/recensement_in     [                 <=>]  11.65M   131KB/s               
ata/recensement_ind     [                <=> ]  11.68M   126KB/s               
ta/recensement_indi     [               <=>  ]  11.75M   131KB/s               
a/recensement_indiv     [              <=>   ]  11.78M   131KB/s               
/recensement_indivi     [             <=>    ]  11.81M   126KB/s               
recensement_individ     [            <=>     ]  11.87M   129KB/s               
ecensement_individu     [           <=>      ]  11.90M   130KB/s               
censement_individus     [          <=>       ]  11.96M   128KB/s               
ensement_individus.     [         <=>        ]  12.00M   129KB/s               
nsement_individus.c     [        <=>         ]  12.03M   129KB/s               
sement_individus.cs     [       <=>          ]  12.09M   129KB/s               
ement_individus.csv     [      <=>           ]  12.12M   129KB/s               
ment_individus.csv      [     <=>            ]  12.15M   129KB/s               
ent_individus.csv       [    <=>             ]  12.21M   127KB/s               
nt_individus.csv        [   <=>              ]  12.25M   127KB/s               
t_individus.csv         [  <=>               ]  12.28M   131KB/s               
_individus.csv          [ <=>                ]  12.31M   128KB/s               
individus.csv           [<=>                 ]  12.37M   133KB/s               
ndividus.csv            [ <=>                ]  12.40M   131KB/s               
dividus.csv             [  <=>               ]  12.46M   134KB/s               
ividus.csv              [   <=>              ]  12.50M   132KB/s               
vidus.csv               [    <=>             ]  12.56M   137KB/s               
idus.csv                [     <=>            ]  12.59M   133KB/s               
dus.csv                 [      <=>           ]  12.62M   133KB/s               
us.csv                  [       <=>          ]  12.68M   136KB/s               
s.csv                   [        <=>         ]  12.71M   133KB/s               
.csv                    [         <=>        ]  12.75M   132KB/s               
csv                     [          <=>       ]  12.81M   133KB/s               
sv                      [           <=>      ]  12.84M   134KB/s               
v                       [            <=>     ]  12.87M   129KB/s               
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                  d     [              <=>   ]  12.96M   128KB/s               
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              data/     [                <=> ]  13.12M   133KB/s               
             data/r     [               <=>  ]  13.18M   135KB/s               
            data/re     [              <=>   ]  13.21M   133KB/s               
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         data/recen     [           <=>      ]  13.34M   134KB/s               
        data/recens     [          <=>       ]  13.37M   130KB/s               
       data/recense     [         <=>        ]  13.43M   134KB/s               
      data/recensem     [        <=>         ]  13.46M   132KB/s               
     data/recenseme     [       <=>          ]  13.53M   133KB/s               
    data/recensemen     [      <=>           ]  13.56M   132KB/s               
   data/recensement     [     <=>            ]  13.59M   132KB/s               
  data/recensement_     [    <=>             ]  13.65M   133KB/s               
 data/recensement_i     [   <=>              ]  13.68M   131KB/s               
data/recensement_in     [  <=>               ]  13.71M   135KB/s               
ata/recensement_ind     [ <=>                ]  13.78M   137KB/s               
ta/recensement_indi     [<=>                 ]  13.81M   137KB/s               
a/recensement_indiv     [ <=>                ]  13.84M   137KB/s               
/recensement_indivi     [  <=>               ]  13.90M   138KB/s               
recensement_individ     [   <=>              ]  13.93M   139KB/s               
ecensement_individu     [    <=>             ]  13.96M   137KB/s               
censement_individus     [     <=>            ]  14.03M   138KB/s               
ensement_individus.     [      <=>           ]  14.06M   137KB/s               
nsement_individus.c     [       <=>          ]  14.09M   136KB/s               
sement_individus.cs     [        <=>         ]  14.15M   137KB/s               
ement_individus.csv     [         <=>        ]  14.18M   134KB/s               
ment_individus.csv      [          <=>       ]  14.25M   140KB/s               
ent_individus.csv       [           <=>      ]  14.28M   135KB/s               
nt_individus.csv        [            <=>     ]  14.31M   131KB/s               
t_individus.csv         [             <=>    ]  14.37M   133KB/s               
_individus.csv          [              <=>   ]  14.40M   133KB/s               
individus.csv           [               <=>  ]  14.43M   132KB/s               
ndividus.csv            [                <=> ]  14.50M   123KB/s               
dividus.csv             [                 <=>]  14.53M   123KB/s               
ividus.csv              [                <=> ]  14.56M   123KB/s               
vidus.csv               [               <=>  ]  14.62M   122KB/s               
idus.csv                [              <=>   ]  14.65M   123KB/s               
dus.csv                 [             <=>    ]  14.68M   123KB/s               
us.csv                  [            <=>     ]  14.75M   124KB/s               
s.csv                   [           <=>      ]  14.78M   123KB/s               
.csv                    [          <=>       ]  14.84M   129KB/s               
csv                     [         <=>        ]  14.87M   124KB/s               
sv                      [        <=>         ]  14.90M   122KB/s               
v                       [       <=>          ]  14.96M   126KB/s               
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       data/recense     [      <=>           ]  15.56M   143KB/s               
      data/recensem     [       <=>          ]  15.59M   141KB/s               
     data/recenseme     [        <=>         ]  15.62M   135KB/s               
    data/recensemen     [         <=>        ]  15.68M   136KB/s               
   data/recensement     [          <=>       ]  15.71M   134KB/s               
  data/recensement_     [           <=>      ]  15.75M   132KB/s               
 data/recensement_i     [            <=>     ]  15.81M   141KB/s               
data/recensement_in     [             <=>    ]  15.84M   136KB/s               
ata/recensement_ind     [              <=>   ]  15.87M   137KB/s               
ta/recensement_indi     [               <=>  ]  15.93M   141KB/s               
a/recensement_indiv     [                <=> ]  15.96M   138KB/s               
/recensement_indivi     [                 <=>]  16.00M   137KB/s               
recensement_individ     [                <=> ]  16.06M   143KB/s               
ecensement_individu     [               <=>  ]  16.09M   134KB/s               
censement_individus     [              <=>   ]  16.15M   141KB/s               
ensement_individus.     [             <=>    ]  16.18M   135KB/s               
nsement_individus.c     [            <=>     ]  16.21M   135KB/s               
sement_individus.cs     [           <=>      ]  16.28M   140KB/s               
ement_individus.csv     [          <=>       ]  16.31M   133KB/s               
ment_individus.csv      [         <=>        ]  16.34M   134KB/s               
ent_individus.csv       [        <=>         ]  16.40M   132KB/s               
nt_individus.csv        [       <=>          ]  16.43M   132KB/s               
t_individus.csv         [      <=>           ]  16.46M   133KB/s               
_individus.csv          [     <=>            ]  16.53M   138KB/s               
individus.csv           [    <=>             ]  16.56M   136KB/s               
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dividus.csv             [  <=>               ]  16.65M   137KB/s               
ividus.csv              [ <=>                ]  16.68M   135KB/s               
vidus.csv               [<=>                 ]  16.71M   135KB/s               
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dus.csv                 [  <=>               ]  16.81M   134KB/s               
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v                       [        <=>         ]  17.06M   123KB/s               
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data/recensement_in     [          <=>       ]  17.14M   125KB/s    in 2m 8s   

2022-05-24 05:25:53 (137 KB/s) - ‘data/recensement_individus.csv’ saved [17971968]
!wget "https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_logements.csv
--2022-05-24 05:25:53--  https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_logements.csv’


          data/rece     [<=>                 ]       0  --.-KB/s               
         data/recen     [ <=>                ]  36.68K   178KB/s               
        data/recens     [  <=>               ] 100.67K   245KB/s               
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  data/recensement_     [        <=>         ] 436.61K   168KB/s               
 data/recensement_l     [         <=>        ] 468.60K   157KB/s               
data/recensement_lo     [          <=>       ] 532.60K   162KB/s               
ata/recensement_log     [           <=>      ] 596.59K   170KB/s               
ta/recensement_loge     [            <=>     ] 692.57K   179KB/s               
a/recensement_logem     [             <=>    ] 788.55K   186KB/s               
/recensement_logeme     [              <=>   ] 892.55K   194KB/s               
recensement_logemen     [               <=>  ] 988.55K   198KB/s               
ecensement_logement     [                <=> ]   1.06M   202KB/s               
censement_logements     [                 <=>]   1.18M   212KB/s               
ensement_logements.     [                <=> ]   1.28M   214KB/s               
nsement_logements.c     [               <=>  ]   1.37M   217KB/s               
sement_logements.cs     [              <=>   ]   1.46M   220KB/s               
ement_logements.csv     [             <=>    ]   1.56M   223KB/s               
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logements.csv           [       <=>          ]   2.15M   266KB/s               
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gements.csv             [     <=>            ]   2.34M   267KB/s               
ements.csv              [    <=>             ]   2.46M   272KB/s               
ments.csv               [   <=>              ]   2.56M   273KB/s               
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data/recensement_lo     [         <=>        ]   4.96M   228KB/s               
ata/recensement_log     [        <=>         ]   5.03M   225KB/s               
ta/recensement_loge     [       <=>          ]   5.06M   223KB/s               
a/recensement_logem     [      <=>           ]   5.12M   213KB/s               
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recensement_logemen     [    <=>             ]   5.21M   201KB/s               
ecensement_logement     [   <=>              ]   5.28M   197KB/s               
censement_logements     [  <=>               ]   5.31M   186KB/s               
ensement_logements.     [ <=>                ]   5.37M   183KB/s               
nsement_logements.c     [<=>                 ]   5.43M   178KB/s               
sement_logements.cs     [ <=>                ]   5.46M   170KB/s               
ement_logements.csv     [  <=>               ]   5.53M   164KB/s               
ment_logements.csv      [   <=>              ]   5.56M   156KB/s               
ent_logements.csv       [    <=>             ]   5.62M   152KB/s               
nt_logements.csv        [     <=>            ]   5.65M   149KB/s               
t_logements.csv         [      <=>           ]   5.71M   151KB/s               
_logements.csv          [       <=>          ]   5.78M   152KB/s               
logements.csv           [        <=>         ]   5.81M   148KB/s               
ogements.csv            [         <=>        ]   5.87M   153KB/s               
gements.csv             [          <=>       ]   5.90M   149KB/s               
ements.csv              [           <=>      ]   5.96M   153KB/s               
ments.csv               [            <=>     ]   6.03M   154KB/s               
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data/recensement_lo     [      <=>           ]   7.40M   153KB/s               
ata/recensement_log     [       <=>          ]   7.43M   149KB/s               
ta/recensement_loge     [        <=>         ]   7.50M   155KB/s               
a/recensement_logem     [         <=>        ]   7.53M   149KB/s               
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recensement_logemen     [           <=>      ]   7.65M   157KB/s               
ecensement_logement     [            <=>     ]   7.68M   154KB/s               
censement_logements     [             <=>    ]   7.75M   158KB/s               
ensement_logements.     [              <=>   ]   7.78M   155KB/s               
nsement_logements.c     [               <=>  ]   7.84M   158KB/s               
sement_logements.cs     [                <=> ]   7.90M   158KB/s               
ement_logements.csv     [                 <=>]   7.93M   158KB/s               
ment_logements.csv      [                <=> ]   8.00M   157KB/s               
ent_logements.csv       [               <=>  ]   8.03M   152KB/s               
nt_logements.csv        [              <=>   ]   8.09M   155KB/s               
t_logements.csv         [             <=>    ]   8.14M   154KB/s               
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logements.csv           [           <=>      ]   8.25M   159KB/s               
ogements.csv            [          <=>       ]   8.28M   149KB/s               
gements.csv             [         <=>        ]   8.34M   155KB/s               
ements.csv              [        <=>         ]   8.37M   146KB/s               
ments.csv               [       <=>          ]   8.43M   152KB/s               
ents.csv                [      <=>           ]   8.50M   141KB/s               
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data/recensement_lo     [             <=>    ]   9.81M   148KB/s               
ata/recensement_log     [            <=>     ]   9.87M   156KB/s               
ta/recensement_loge     [           <=>      ]   9.90M   152KB/s               
a/recensement_logem     [          <=>       ]   9.96M   157KB/s               
/recensement_logeme     [         <=>        ]  10.00M   152KB/s               
recensement_logemen     [        <=>         ]  10.06M   155KB/s               
ecensement_logement     [       <=>          ]  10.12M   155KB/s               
censement_logements     [      <=>           ]  10.15M   148KB/s               
ensement_logements.     [     <=>            ]  10.21M   154KB/s               
nsement_logements.c     [    <=>             ]  10.25M   147KB/s               
sement_logements.cs     [   <=>              ]  10.31M   151KB/s               
ement_logements.csv     [  <=>               ]  10.37M   145KB/s               
ment_logements.csv      [ <=>                ]  10.42M   145KB/s               
ent_logements.csv       [<=>                 ]  10.46M   147KB/s               
nt_logements.csv        [ <=>                ]  10.50M   142KB/s               
t_logements.csv         [  <=>               ]  10.56M   146KB/s               
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logements.csv           [    <=>             ]  10.65M   147KB/s               
ogements.csv            [     <=>            ]  10.71M   147KB/s               
data/recensement_lo     [      <=>           ]  10.77M   152KB/s    in 62s     

2022-05-24 05:26:57 (178 KB/s) - ‘data/recensement_logements.csv’ saved [11293731]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data/recensement_individus.csv")
df['COUPLE'] = df['COUPLE'].astype("category")
df['COUPLE'] = df['COUPLE'].cat.rename_categories({1: 'Vit en couple', 2: 'ne vit pas en couple'})

df['CS24'] = df['CS24'].astype("category")
df['CS24'] = df['CS24'].cat.rename_categories({10: 'Agriculteurs exploitants', 21: 'Artisans', 22: 'Commerçants et assimilés', 23: 'Chefs d\'entreprise de 10 salariés ou plus',
                                               31: 'Professions libérales et assimilés', 32: 'Cadres de la fonction publique, professions intellectuelles et artistiques',
                                               36: 'Cadres d\'entreprise', 41: 'Professions intermédiaires de l\'enseignement, de la santé, de la fonction publique et assimilés',
                                               46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
                                               48: 'Contremaîtres, agents de maîtrise', 51: 'Employés de la fonction publique',
                                               54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce', 56: 'Personnels des services directs aux particuliers',
                                               61: 'Ouvriers qualifiés', 66: 'Ouvriers non qualifiés', 69: 'Ouvriers agricoles'})

df['CS42'] = df['CS42'].astype("category")
df['CS42'] = df['CS42'].cat.rename_categories({11: 'Agriculteurs sur petites exploitations', 12: 'Agriculteurs sur moyennes exploitations', 13: 'Agriculteurs sur grandes exploitations',
                                               21: 'Artisans', 22: 'Commerçants et assimilés' ,23: 'Chefs d\'entreprise de 10 salariés ou plus',
                                               31: 'Professions libérales et assimilés', 33: 'Cadres de la fonction publique', 34: 'Professeurs, professions scientifiques',
                                               35: 'Professions de l\'information, des arts et des spectacles', 37: 'Cadres administratifs et commerciaux d\'entreprise',
                                               38: 'Ingénieurs et cadres techniques d\'entreprise', 42: 'Professeurs des écoles, instituteurs et assimilés',
                                               43: 'Professions intermédiaires de la santé et du travail social',
                                               44: 'Clergé, religieux', 45: 'Professions intermédiaires administratives de la fonction publique',
                                               46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
                                               48: 'Contremaîtres, agents de maîtrise', 52: 'Employés civils et agents de service de la fonction publique',
                                               53: 'Policiers et militaires', 54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce',
                                               56: 'Personnels des services directs aux particuliers', 62: 'Ouvriers qualifiés de type industriel',
                                               63: 'Ouvriers qualifiés de type artisanal', 64: 'Chauffeurs', 65: 'Ouvriers qualifiés de la manutention, du magasinage et du transport',
                                               67: 'Ouvriers non qualifiés de type industriel', 68: 'Ouvriers non qualifiés de type artisanal',
                                               69: 'Ouvriers agricoles'})


df['CS8'] = df['CS8'].astype("category")
df['CS8'] = df['CS8'].cat.rename_categories({1: 'Agriculteurs exploitants', 2: 'Artisans, commerçants et chefs d\'entreprise',
                                             3: 'Cadres et professions intellectuelles supérieures', 4 : 'Professions Intermédiaires',
                                             5: 'Employés', 6: 'Ouvriers'})


df['CSSAL'] = df['CSSAL'].astype("category")
df['CSSAL'] = df['CSSAL'].cat.rename_categories({1: 'Manœuvre, ouvrier spécialisé', 2: 'Ouvrier qualifié ou hautement qualifié, technicien d’atelier',
                                                 3: 'Technicien (non cadre)', 4 : 'Agent de catégorie B de la fonction publique',
                                                 5: 'Agent de maîtrise, maîtrise administrative ou commerciale, VRP', 6: 'Agent de catégorie A de la fonction publique',
                                                 7: 'Ingénieur, cadre d’entreprise', 8: 'Agent de catégorie C ou D de la fonction publique',
                                                 9: 'Employé (par exemple : de bureau, de commerce, de la restauration, de maison)'})

EMPL_labels = {3: 'Artisan, commerçant, industriel, travailleur indépendant', 4: 'Stagiaire rémunéré, apprenti sous contrat',
                5: 'Salarié du secteur privé à durée déterminée', 6: 'Salarié du secteur privé à durée indéterminée',
                7 : 'Salarié du secteur public à durée déterminée', 8: 'Salarié du secteur public à durée indéterminé', }
df['EMPL'] = df['EMPL'].astype("category")


df['DIPL'] = df['DIPL'].astype("category")
diplomes_libelles = {1: 'Pas de scolarisation', 2: 'Aucun diplôme mais scolarisation jusqu’en primaire', 3: 'Aucun diplôme mais scolarisation jusqu’au collège', 
            4: 'Aucun diplôme mais scolarisation au-delà du collège', 11: 'CEP' , 12: 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 13: 'CAP, BEP ou diplôme de niveau équivalent',
            14: 'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
            15: 'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
            16: 'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
            17: 'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
            18: 'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
            19: 'Doctorat de recherche (hors santé)'}
#df['DIPL'] = df['DIPL'].cat.rename_categories(diplomes_libelles)




#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })

#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })


df.head()
ID IDLOG AGEA AGER ANNINS APE CNAT COUPLE CPAYSN CPAYSRA ... STAT STATANT STM TACT TP TRAANT TRANS TYP TYPEMPL TYPMENR
0 200296 85580.0 7 7 NaN NaN NaN NaN NaN NaN ... NaN NaN 6 NaN NaN NaN NaN 2 NaN 3.0
1 200301 48282.0 46 46 NaN 2410Z NaN Vit en couple NaN NaN ... 3.0 NaN 2 1.0 1.0 NaN 4.0 2 1.0 3.0
2 200307 477.0 15 15 NaN NaN NaN ne vit pas en couple NaN NaN ... NaN NaN 6 3.0 NaN 2.0 5.0 2 NaN 3.0
3 200315 94323.0 40 40 NaN 8411Z NaN Vit en couple NaN NaN ... 3.0 NaN 3 1.0 1.0 NaN 4.0 2 1.0 3.0
4 200318 107640.0 68 68 1972.0 NaN NaN Vit en couple NaN NaN ... NaN 1.0 1 5.0 NaN 1.0 4.0 2 NaN 5.0

5 rows × 43 columns

df.describe()
ID IDLOG AGEA AGER ANNINS CNAT CPAYSN CPAYSRA EXER GAD ... STAT STATANT STM TACT TP TRAANT TRANS TYP TYPEMPL TYPMENR
count 203144.000000 199929.000000 203144.000000 203144.000000 44082.000000 2412.000000 3137.00000 131.000000 89638.000000 203144.000000 ... 89155.000000 41032.000000 203144.000000 162368.000000 89638.000000 61332.000000 162852.000000 203144.000000 68942.000000 199844.000000
mean 135704.624434 54598.597797 35.574174 35.283617 1999.589651 432.129353 476.13803 454.458015 1.081428 3.085555 ... 2.755224 1.116494 3.879258 2.618632 1.114003 1.281729 3.701183 2.016245 1.252894 3.270726
std 78350.449683 31455.117174 21.826895 21.821489 17.898269 268.332958 76.04784 124.236639 0.328495 2.186296 ... 0.810840 0.379688 2.129446 1.953191 0.317817 0.449846 1.125026 0.126415 0.717101 1.215611
min 1.000000 2.000000 0.000000 0.000000 1927.000000 103.000000 127.00000 132.000000 1.000000 0.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 2.000000 1.000000 1.000000
25% 67790.500000 27344.000000 17.000000 17.000000 1988.000000 219.000000 501.00000 501.000000 1.000000 1.000000 ... 3.000000 1.000000 1.000000 1.000000 1.000000 1.000000 4.000000 2.000000 1.000000 3.000000
50% 135765.000000 54590.000000 35.000000 34.000000 2006.000000 416.000000 514.00000 501.000000 1.000000 3.000000 ... 3.000000 1.000000 4.000000 1.000000 1.000000 1.000000 4.000000 2.000000 1.000000 3.000000
75% 203519.250000 81991.000000 52.000000 51.000000 2015.000000 514.000000 514.00000 501.000000 1.000000 5.000000 ... 3.000000 1.000000 6.000000 5.000000 1.000000 2.000000 4.000000 2.000000 1.000000 4.000000
max 271406.000000 109025.000000 104.000000 104.000000 2019.000000 999.000000 514.00000 514.000000 3.000000 9.000000 ... 9.000000 3.000000 6.000000 7.000000 2.000000 2.000000 5.000000 3.000000 9.000000 5.000000

8 rows × 30 columns

from dataprep.eda import plot
plot(df)
DataPrep.EDA Report
Dataset Statistics
Number of Variables 43
Number of Rows 203144
Missing Cells 3.5524e+06
Missing Cells (%) 40.7%
Duplicate Rows 0
Duplicate Rows (%) 0.0%
Total Size in Memory 103.8 MB
Average Row Size in Memory 535.9 B
Variable Types
  • Numerical: 9
  • Categorical: 34
Dataset Insights
ID is uniformly distributed Uniform
AGEA and AGER have similar distributions Similar Distribution
CNAT and CPAYSN have similar distributions Similar Distribution
IDLOG has 3215 (1.58%) missing values Missing
ANNINS has 159062 (78.3%) missing values Missing
APE has 156869 (77.22%) missing values Missing
CNAT has 200732 (98.81%) missing values Missing
COUPLE has 40776 (20.07%) missing values Missing
CPAYSN has 200007 (98.46%) missing values Missing
CPAYSRA has 203013 (99.94%) missing values Missing
Dataset Insights
CS24 has 128604 (63.31%) missing values Missing
CS42 has 128604 (63.31%) missing values Missing
CS8 has 128604 (63.31%) missing values Missing
CSSAL has 140423 (69.12%) missing values Missing
DIPL has 40776 (20.07%) missing values Missing
EMPL has 113506 (55.87%) missing values Missing
EXER has 113506 (55.87%) missing values Missing
IRA has 13748 (6.77%) missing values Missing
MINE has 199871 (98.39%) missing values Missing
PROVRA has 13748 (6.77%) missing values Missing
Dataset Insights
PROVTRA has 113506 (55.87%) missing values Missing
RECH has 144665 (71.21%) missing values Missing
SAL has 191662 (94.35%) missing values Missing
SCOL has 26951 (13.27%) missing values Missing
SECT10 has 113506 (55.87%) missing values Missing
SECT21 has 113506 (55.87%) missing values Missing
SECT5 has 113506 (55.87%) missing values Missing
STAT has 113989 (56.11%) missing values Missing
STATANT has 162112 (79.8%) missing values Missing
TACT has 40776 (20.07%) missing values Missing
Dataset Insights
TP has 113506 (55.87%) missing values Missing
TRAANT has 141812 (69.81%) missing values Missing
TRANS has 40292 (19.83%) missing values Missing
TYPEMPL has 134202 (66.06%) missing values Missing
TYPMENR has 3300 (1.62%) missing values Missing
ANNINS is skewed Skewed
CNAT is skewed Skewed
CPAYSN is skewed Skewed
GAD is skewed Skewed
APE has a high cardinality: 369 distinct values High Cardinality
Dataset Insights
MINE has constant value "1.0" Constant
PROV has constant value "Sud" Constant
APE has constant length 5 Constant Length
CPAYSRA has constant length 5 Constant Length
EMPL has constant length 3 Constant Length
EXER has constant length 3 Constant Length
GENRE has constant length 1 Constant Length
ILN has constant length 1 Constant Length
IRA has constant length 3 Constant Length
MINE has constant length 3 Constant Length
Dataset Insights
NAT has constant length 1 Constant Length
PROV has constant length 3 Constant Length
RECH has constant length 3 Constant Length
SAL has constant length 3 Constant Length
SCOL has constant length 3 Constant Length
SECT10 has constant length 2 Constant Length
SECT21 has constant length 1 Constant Length
SECT5 has constant length 3 Constant Length
STAT has constant length 3 Constant Length
STATANT has constant length 3 Constant Length
Dataset Insights
STM has constant length 1 Constant Length
TACT has constant length 3 Constant Length
TP has constant length 3 Constant Length
TRAANT has constant length 3 Constant Length
TRANS has constant length 3 Constant Length
TYP has constant length 1 Constant Length
TYPEMPL has constant length 3 Constant Length
TYPMENR has constant length 3 Constant Length
GAD has 28705 (14.13%) zeros Zeros
GAQ has 13748 (6.77%) zeros Zeros
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Number of plots per page:

#df2 = df[['ID', 'DIPL', 'EMPL']]
#df2 = df2.fillna(0)
#df2.head()
#df2 = df2.pivot_table('DIPL', 'EMPL', 'ID', aggfunc="sum")

#f, ax = plt.subplots(figsize=(9, 6))
#sns.heatmap(df2, annot=True, linewidths=.5, ax=ax)
df_age = df[['AGER', 'GENRE']]
df_homme = df_age.loc[df_age['GENRE'] == 1].groupby('AGER').sum()
df_homme['AGER'] = df_homme.index
print(df_homme.shape)
print(df_homme.loc[df_homme['AGER'] == 40])
df_homme['GENRE'] = 0-df_homme['GENRE']
df_homme = df_homme.rename(columns={'GENRE': 'homme'})


df_femme = df_age.loc[df_age['GENRE'] == 2].groupby('AGER').sum()
df_femme = df_femme.rename(columns={'GENRE': 'femme'})
df_femme['AGER'] = df_femme.index
print(df_femme.shape)
df_femme.loc[df_femme['AGER'] == 40]
(101, 2)
      GENRE  AGER
AGER             
40     1349    40
(105, 2)
femme AGER
AGER
40 3094 40

Pyramide des ages

sns.set(font_scale = 2)
df4 = pd.concat([df_homme, df_femme], axis=1).iloc[::-1]
figure = plt.figure(figsize=(50, 50))

bar_plot = sns.barplot(x='homme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot = sns.barplot(x='femme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot.set(ylabel="Age", xlabel="Nombre de personnes", title = "Pyramide des âges")
plt.plot([0,0], [0,105], linewidth=2)
#sns;barplot(data=df_age, x=)
[<matplotlib.lines.Line2D at 0x7f94bed01df0>]
_images/recensement_11_1.png

Relation entre niveau de diplome, type d’emploi et catégorie socioprofessionnelle

df_metier = df[['DIPL', 'EMPL', 'CS8']].dropna()
sns.set(font_scale = 2)
fig, ax = plt.subplots(figsize=(50, 30))
df_counts = df_metier.groupby(['DIPL', 'EMPL']).size().reset_index()
df_counts.columns.values[df_counts.columns == 0] = 'count'
scale = 500*df_counts['count'].size
size = df_counts['count']/df_counts['count'].sum()*scale
#size = size.astype(float)

#sns.stripplot(x='DIPL', y='EMPL', hue='CS8', data=df_metier, ax=ax) #, size=size, sizes=(10,500)
dipl_id = [1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]
dipl_lbl = ['Pas de scolarisation', 'Aucun diplôme mais scolarisation jusqu’en primaire', 'Aucun diplôme mais scolarisation jusqu’au collège', 
            'Aucun diplôme mais scolarisation au-delà du collège', 'CEP' , 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 'CAP, BEP ou diplôme de niveau équivalent',
            'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
            'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
            'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
            'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
            'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
            'Doctorat de recherche (hors santé)']
#plt.xticks(dipl_id, dipl_lbl, rotation=45, )

empl_lbl = ['Artisan, commerçant, industriel, travailleur indépendant', 'Stagiaire rémunéré, apprenti sous contrat',
            'Salarié du secteur privé à durée déterminée', 'Salarié du secteur privé à durée indéterminée',
            'Salarié du secteur public à durée déterminée', 'Salarié du secteur public à durée indéterminé']


from sklearn.preprocessing import OrdinalEncoder
import numpy as np
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df_metier), columns=list(df_metier.columns))

xnoise, ynoise = np.random.random(len(df_metier))/2, np.random.random(len(df_metier))/2 

sns.scatterplot(enc_df["DIPL"]+xnoise, enc_df["EMPL"]+ynoise, alpha=0.5, hue=enc_df['CS8'], palette="hls")

plt.yticks(np.arange(0.25, len(empl_lbl)+0.25, 1), empl_lbl)

xrange = np.arange(0.25, len(dipl_lbl)+0.25, 1)
plt.xticks(xrange, dipl_lbl, rotation=90)

plt.legend(title='Categories socioprofessionnelles', loc='lower left', labels=['Agriculteurs exploitants', 'Artisans, commerçants et chefs d\'entreprise',
                                                         'Cadres et professions intellectuelles supérieures', 'Professions Intermédiaires',
                                                         'Employés', 'Ouvriers'])
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(
<matplotlib.legend.Legend at 0x7f94deb0bf40>
_images/recensement_13_2.png